The Role of Trust in Financial Sector Development

In a decentralized-decisions economic environment, agents consider the risk that others
might unfairly exploit informational asymmetries to their own advantage. Incomplete trust,
affects, in particular, financial transactions whereby agents trade current real claims for promises
of future real claims. Agents thus invest considerable resources to assess the trustworthiness of
others with whom they know they can interact only under conditions of limited and
asymmetrically distributed information, and to ensure compliance with contractual obligations.
This study analyzes how incomplete trust shapes transaction costs in trading assets, and how it
affects intertemporal resource allocation and asset pricing decisions from rational, forward-
looking agents. The analysis leads to some core...

Nội dung Text: The Role of Trust in Financial Sector Development

The role of trust in financial sector development
by
Biagio Bossone
World Bank
Summary
In a decentralized-decisions economic environment, agents consider the risk that others
might unfairly exploit informational asymmetries to their own advantage. Incomplete trust,
affects, in particular, financial transactions whereby agents trade current real claims for promises
of future real claims. Agents thus invest considerable resources to assess the trustworthiness of
others with whom they know they can interact only under conditions of limited and
asymmetrically distributed information, and to ensure compliance with contractual obligations.
This study analyzes how incomplete trust shapes transaction costs in trading assets, and how it
affects intertemporal resource allocation and asset pricing decisions from rational, forward-
looking agents. The analysis leads to some core propositions on the role of finance and financial
efficiency for economic development. The study identifies policies for financial sector reforms in
emerging economies, aimed to improve the efficiency of the financial system in dealing with
incomplete trust.
I wish to thank J. Caprio, P. Honohan, M. Ghirga , and C. Schioppa for their helpful comments on
previous versions of this work. Of course, I remain the only responsible for any errors and the
opinions expressed in the text, which do not necessarily reflect those of the World Bank. As usual,
and more than usual, I owe profound gratitude to my wife Ornella for her support.

“In a society with growing choices, and one
where the depth of information is potentially
infinite, the highest value will be given to the
source whose information is most dependable”
(T. Rosentiel and B. Kovach).1
Part I. Finance, information, and trust
I.1 Asymmetric information and incomplete trust
Although the importance of information imperfections in economic analysis had been early
recognized by some scholars (Arrow, 1953; Stigler, 1961; Brunner and Meltzer, 1971; Alchian
and Demsetz, 1972), it was only after research started systematically to look at the implications of
the asymmetric distribution of information across the agents, well into the eighties, that
information has become central to the study of finance. The essence of the Asymmetric
Information (AI) research program in finance is captured by Greenwald and Stiglitz’ (1987)
emphasis on “…the need for a more radical departure from the neoclassical framework, and for a
much deeper study of the consequences of imperfections in capital markets, imperfections which
can be explained by the costs of information” (p. 123).
AI characterizes a situation where one side of the market has better information than the
other on options and incentives, and the less informed side is aware of its informational
disadvantage. AI in financial markets creates inefficiencies because the incentives problems due
to adverse selection and moral hazard of fund-users diminish the net returns to financial investors.
Such incentive problems reduce the opportunity for risk sharing in capital markets and may give
rise to fund-rationing as financial investors choose to turn away observationally equivalent
borrowers at a fixed rate of interest, or discriminate among borrowers with respect to the terms of
the financial agreements.
By challenging the assumption of perfect and costless information, AI theory has derived
important implications for understanding rational behavior under risk, also shedding light on the
principal-agent problems underpinning a large variety of real-world agent interactions. In
particular, AI theory has laid the ground for a micro-economically founded interpretation of the
role of financial intermediaries (especially banks) as reducing risks on financial investment
through information processing, and has laid out fundamental principles for designing appropriate
incentives to limit the inefficiencies from information asymmetries.
2

Yet, the existence of information asymmetries is only part of the more general problem of
uncertainty confronting rational choices in a world with time. Post Keynesians radically criticize
AI (New Keynesian) theorists for eluding the question of how agents form their decisions in the
absence of risk mapping information, by assuming a (typically neoclassical) ergodic world where
well-defined and known probability distributions of future contingencies replace Keynesian
(fundamental) uncertainty with manageable risk.2 Moreover, Post Keynesians deny that the
implications of AI theory say anything on economic cycles and crises that is not already
embodied in Keynes’ liquidity-preference theory.
Recognizing that information is intrinsically limited and asymmetric, and that
diversification and specialization of activity require agents to rely on the services of others, this
study looks at the implications for intertemporal resource allocation and asset prices of that
particular form of uncertainty deriving from lack of full trust - or incomplete trust - among
agents. Incomplete trust can be defined as the agents’ awareness that others may seek to pursue
inappropriate gains either through deliberately reneging on obligations due on earlier
commitments, or by hiding information relevant to transactions. More in general, and considering
that agents operate under uncertainty, the concept of trust may involve the agent’s judgement that
her counterparty to a contract would make all reasonable efforts to deliver on the contract. 3
Incomplete trust lies at the core of AI. Whereas, in principle, full trustworthy agents can
trade at zero (or low) cost, in spite of AI, since each can (risklessly) take everybody else’s word at
face value, symmetric information is not possible without assuming full trust: full informational
symmetry holds only if individuals always reveal their true inner motives, that is, if they are fully
trustworthy.
As the risk of unfair exploitation of asymmetries grows real, what becomes important to
the agents is to be able to benefit from asymmetric information while managing their mutual trust
gaps. More information is searched by the agents not so much to reduce information asymmetries,
as to see whether and how they can trust each other, some agents specialize in bridging these
gaps, and institutions evolve to reduce the effects of distrust by improving incentives to honest or
fully informative behavior and contractual performance. Resources invested in activities aimed to
reduce the inefficiency costs from incomplete trust affect transactions and the price of
commodities and assets exchanged among the assets
The problem of incomplete trust is particularly crucial in financial transactions, where
anonymous agents trade current real resource claims in exchange for promises to receive back
real resource claims at some given point in future. Traders of promises need to ascertain whether
their counterparties do their best to keep to their promises, and whether they are able to use
information efficiently to this end.
3

In small communities of societies in early stages of economic development, with strong
ties among members, financial transactions even with long-term obligations can take place at
reasonable costs.4 In contrast, in larger and more complex communities, where interpersonal
bonds are weakened by agent anonymity and information asymmetries are embedded in largely
decentralized-decisions contexts, financial transactions can be undertaken only if institutions
specialize in activities to limit the costs of incomplete trust and if they are supported by
appropriate infrastructure.
Specializing means investing in information to select trustworthy and efficient fund-users,
monitoring their behavior, adopting incentives to elicit honest behavior, thus creating a
reputation of their own for being good bridges of trust between anonymous agents.5 Anonymous
financial-service users, on their part, invest more to identify reliable and efficient bridges of trust
with other agents, so as to reduce the costs from AI separating them.
The role of specialized institutions is complemented by infrastructural components (e.g.,
legal system, regulation and supervision, trading technologies, incentives and enforcement
mechanisms) aimed to strengthen commitment compliance from individual agents.
This study analyzes how incomplete trust shapes transaction costs and – hence - asset
allocation, and how the financial system and public policy respond to the problem of incomplete
trust. The study is structured in two parts. In part I, section I.2 discusses the role of money and
financial assets under incomplete trust, develops a model to determine the impact of financial
efficiency on intertemporal resource allocation, asset prices and capital accumulation, and draws
implications for the relationship between finance and economic development. Part II focuses on
policy issues: sections II.1 and II.2 discuss how financial intermediaries can enhance trust by
accumulating reputational capital, and section II.3 identifies policies to improve the efficiency of
the financial system in dealing with incomplete trust.
I.2 Information and trust in intertemporal resource allocation and pricing6
In a world where transactions were carried out simultaneously, money would be redundant.
Money becomes necessary as a store of value and a transaction device only when time is
introduced in the exchange process and the simultaneity assumption is dropped.
The link between time and money rests on a twofold argument. The first - following the
Keynesian-Hicksian tradition - relates to uncertainty and market incompleteness: to the extent
that transactions are effected sequentially, the future is uncertain, and markets do not exist for all
possible contingencies, the agents hold reserves of purchasing power stored in instruments that
can easily be exchanged for real commodities as needs arise.
4

The generally accepted analytical implication of the uncertainty argument is that under the
assumption of complete markets (as in a typical Arrow-Debreu setting), and notwithstanding the
existence of time, money becomes as irrelevant as in timeless models. Gale (1978) has proved
this argument flawed by showing that, even with complete markets, money serves as a store of
value if the agents do not trust each other in their commitment to fulfill their contract terms.7
Incomplete trust is thus the second reason for tying money to time.8 Even if all agents had
perfect forecast capability of future contingencies, their anticipation that others might renege on
obligations, or cheat on the quality of whatever they supply, would by itself create the need to
adopt some means that can store value over time and be equally acceptable to all agents in the
exchange process. Such means must be easily recognizable by all participants, so that its
information content is visible to all at a low cost.9 This requirement would not be necessary in a
world of fully trustworthy individuals, even if information were distributed symmetrically among
them, as each would be taken at her own word as to the value of whatever she supplies in the
exchange process. Of course, individuals’ uncertainty of the future would still make them hold
stores of value for precautionary reasons, or give them incentives to hold assets for speculative
purposes.
Under incomplete trust, information is necessary for the agents to assess their mutual
trustworthiness as well as the quality of the assets submitted to trade.10 In such a case, assets
differ from one another as to their power to convey information on their quality and the
trustworthiness of their suppliers: some assets are relatively more costly than others for use in the
exchange and payment process, since their acceptability requires more information and involves
longer search activity than others. In a competitive equilibrium setting, the asset return structure
should thus reflect the transaction costs underlying each asset’s trading, ultimately associated
with the quality of the assets and the trustworthiness of those for whom they represent a
liability.11 On their side, asset holders should hold more of an asset up to where its return equals
its marginal utility net of what is lost due to the (asset-specific) transaction costs. In fact, one way
to look at the financial system in a decentralized-decisions context is to consider its role to reduce
the transaction costs associated with incomplete trust.
I.2.1 A model of incomplete trust in asset trading
The model used in this study is an integrated version of Bossone’s (1997). As in the former
version, the core of the model consists of deriving a general form of optimal demand functions
for assets trading in a multi-sector economy, under incomplete trust and uncertainty. In the
following, the first step will be to define the rationale for, and the mechanism of, asset price
discounting in an incomplete trust context. As a second step, the utility content of money and
5

financial assets will be formalized. An optimal intertemporal decision framework under general
equilibrium will then be used to study the effects of financial system innovations and output
variability on resource allocation and pricing.
Asset price discounting
Assume an asset Q - say, a financial security - earning a nominal interest rate r Q .12 With
incomplete trust, asset Q may be purchased (sold) at market price p Q ( = 1 / r Q ) plus (minus) a
unit premium (discount) specific to the asset, d Q . To formalize the discount factor d Q , define
first the asset trading context under incomplete trust.
An holder of Q who wishes to realize the asset advertises its sale on the market and submits
an ask-price for it.13 If the holder is fully trusted by all other agents, no prior assessment is
necessary from potential buyers and the transaction costs involved are smaller than under
incomplete trust since Q is equally acceptable to all agents and against all other assets. With
incomplete trust, on the other hand, sellers need to undertake longer and costlier processes for
achieving best price sales as: i) different agents submit different bid-prices for the same asset due
to their different perceptions of asset quality, and ii) not all agents stand ready to buy assets on
sale due to the assets’ limited acceptability in trade. Also, iii) higher costs are incurred by the
agents to assess their mutual trustworthiness as well as the quality of the asset traded. Lastly, iv)
assets with different characteristics bear different information costs.
Under incomplete trust, one may assume that the shorter the time available to the holder for
realizing the asset, and the lower the asset’s acceptability in trade, the larger is the cost that the
holder must be willing to bear in order to raise the needed liquidity. Such a cost takes the form of
a discount on the asset market price that the seller offers to potential buyers. The rationale for
price discounting rests on two factors: a) risk-averse agents are reluctant to trade assets whose
true value they know with certainty against assets with uncertain or unknown value to them; b)
even if some agents possess sure knowledge of the true value of the asset, they consider that
others might not share the same knowledge thereby introducing frictions in the indirect exchange
of the asset.
The time factor plays here a crucial role: with incomplete trust and a given financial
structure (as characterized by institutional and legal arrangements, range and quality of
intermediaries, and transaction technologies), each asset is characterized by its own optimal
transaction time. This is the minimum time required of the asset-holder to maximize the net
proceeds from the asset sale, including as such the time it takes the buyer to assess the
6

trustworthiness of the seller, the quality of the asset, the asset’s acceptability in indirect exchange,
as well as the time necessary to complete the transaction.14
Operationally, the optimal transaction time can be defined as the interval necessary for the
seller to realize the asset at its current market value. The proceeds from optimal asset sale equal
such a price net of the minimum asset-specific (unit) transaction cost d Q* , involved in
completing the sale in the given trading context. Thus, suboptimal sales that take place at
discounts on the asset market price greater than the minimum transaction cost, and occur when
the asset must be realized within a time span shorter than its optimal transaction time. Discount
factor d Q ≥ d Q* can thus be formalized as a function of:
1. the optimal transaction time interval, ∆t Q : the longer the latter, the larger the discount at
*
which Q must trade to shorten the sale time; and
2. the time interval, ∆t Q , available to Q’s holder to realize the asset: for ∆t Q < ∆t Q , the shorter
0 0 *
the former vis-à-vis the latter, the larger the discount at which Q sells.
Expectations of higher output variability affect the price discount factor by shortening ∆t Q .
0
Thus,
(1) d tQ = d Q (1 − ∆t°(σ t→ | wt ) / ∆tQ (Ψ))
*
where (σ t→ | wt ) = t E[ β t +iσ t +i | wt ]i∞ 1 reflects the agent’s expected (time weighted) average
=
variability of consumption from date t onward, conditional on signal wt . The conditional relation
of σ → on w is such that the former increases as w approaches one. Signal w ∈ ( 0,1) varies
directly with the uncertainty perceived by the agents in the economy (see Appendix IV). Ψ ∈ R +
indicates the level of efficiency of the underlying financial system, defined in this context to
reflect the financial system’s capacity to reduce transaction costs related to incomplete trust
through institutions and infrastructure (e.g., markets, technologies, regulations, and enforcement
mechanisms) for the intermediation of financial resources. For our purposes, function (1) can be
simplified as
(1a) d tQ = d Q (σ t→ | wt , Ψ) d ’σ > 0 d ’Ψ < 0
The effect of the financial efficiency indicator is such that, other conditions being equal,
price discount factors for the same assets are larger in less efficient financial systems, that is,
assets’ optimal transaction times are longer under lesser financial efficiency. As a result, all extra
discounts with respect to d Q* on suboptimal sales would be larger. Note that for each Q , the
7

variable and state-contingent price discount would be determined according to the following
conditions:
0≤ d ≤1
d= d * if ∆t ° > ∆t *
d→1 if ∆t °/ ∆t * → 0
Also, it is assumed that d= d * = 0 if ∆t * = 0, that is, perfectly liquid assets trade at zero discount.
In the extreme, where no financial system existed and Ψ approached zero, trading most assets
would present no economic convenience. On the other hand, financial technological and
institutional development (i.e., a higher Ψ ) increases safety and speed of asset trading - and
hence the degree of trust that agents place in asset trading – and reduces asset optimal transaction
times. Note, thus, that the concept of financial efficiency as here defined involves that of safe
trading as well.
Asset utility
In this model, money and financial assets are vehicles used for transferring consumption
decisions across time, to the point where future (contingent) consumption yields the highest
expected marginal utility. They transfer such decision at different speed, or transaction costs. The
structure of such costs is, as discussed above, asset specific, while their scale is determined by the
overall efficiency of the financial system.
Assets produce utility in terms of their power to make consumption accessible to their
holders when and as needed. Utility varies positively with the consumption accessible though the
asset, and negatively with the cost of liquidating the asset. If an agent holds an asset for a period
during which she might incur income shocks, she could use that asset as an option to be exercised
at any point of the period to avert (or limit) consumption losses. To estimate the option’s current
value, the agent needs to conjecture the probability of having to exercise the option (i.e., realize
the asset) at each future date of the holding period and at a given cost. Such probability depends
on the agent’s knowledge of the distribution of future possible shocks, and on her use of current
information to anticipate future shocks.
The probability is defined as follows. Consider a discrete and infinite time horizon [0, ∞ ),
and call sτc ∈ S ⊂ R c ⊗ R τ the date-event whereby at any instant prior to τ the agent expects a
+
consumption shock to be received at τ and mobilizes her resource endowments (that could
otherwise be invested) to support consumption. Let s − c be the complement of s c in S, and let
8

I.2.2 Asset allocation and prices under incomplete trust
To determine the individual resource allocation choices when (trust-related) transaction
costs are incorporated into the agents’ decision-making process, assume an economy with three
sectors - households, government and firms -, one composite commodity C for consumption
expressed in real terms, and three assets expressed in nominal terms: monetary asset L,
government bond B, and corporate financial security A.
Firms are owned by the households. Firms use capital K to produce nominal output Y 0 .16
Firms sell output at price p C and turn their income to households ( Y h ). Households also receive
government income transfer g and pay out lump-sum taxes t to the government. The government
finances income transfers via taxation and bond issues to the households.
Households have well-behaved utility functions with regular shape throughout their
domain, i.e., with u'( ⋅ )>0 and u''( ⋅ )

Rule (7) requires each household to equate at every instant the weighted marginal utilities
derived from allocating the marginal resource unit to the available consumption commodities and
assets (weighted with the inverse of their own current market price). For given expectations of
future shocks to consumption, rule (7) ensures that the costs of mobilizing resources to absorb
those shocks are minimized since the underlying optimization model incorporates the probability
of incurring such costs (relation (2)). At each date, prices in each market must be such that rule
(7) holds across all households under the following market clearing conditions:
(E1) ∑C h
h
= Y 0 = ∑h Y h
(E2) ∑ h
Lh = L0
(E3) ∑ h
Bh = B0
(E4) ∑ h
Ah = A 0
From rule (7) it is immediately evident that a lower Ψ implies at the margin a lower utility
of the illiquid asset. Equilibrium allocation would then involve relatively smaller shares of asset A
in portfolios. Thus, rule (7) implies that:
Proposition 1. Ceteris paribus, in an economy with relatively lower financial efficiency (i.e., a
lower Ψ ) equilibrium current consumption and the equilibrium stocks of liquid assets in
individual portfolios are larger than in an economy with a more efficient financial system.
Call Ψ -efficient (resp., Ψ -inefficient) the economy with high (low) financial efficiency.
The two economies are hypothetically equal in all other respects. As asset A represents a financial
claim on the economy’s productive capital and its supply is interest-elastic (with positive but
finite elasticity), rule (7) and conditions E(1)-E(4) imply that:
Proposition 2. The Ψ -inefficient economy has a smaller equilibrium capital endowment
than Ψ -efficient economy.18
In terms of relative price structure, rule (7) and conditions E(1)-E(4) also imply that:
12

Proposition 3. In the Ψ -inefficient economy capital trades at a discount as compared to the
Ψ -efficient economy. The same equilibrium stock of capital is held in the two economies only if,
ceteris paribus, the return on capital in the Ψ -inefficient economy is enough to compensate
holders for the relative financial inefficiency.
Note however, that:
Proposition 4. The increase in the rate of return required to induce holders of capital in the
Ψ -inefficient economy to catch up with the capital endowment of the Ψ -efficient economy is
not feasible under the existing technology.
The extent of the unfeasible region - defined by the demands for capital in the two
economies and the marginal efficiency of capital - represents the cost of the relative financial
inefficiency of the Ψ -inefficient economy (see Appendix II). The above propositions have clear
implications for economic development policy, as they emphasize the importance of reforms to
enhance financial efficiency as a way to support productive capital accumulation in emerging
economies.
I.2.3 Impact of uncertainty
Uncertainty is here assumed to affect the probability distribution functions used by the
agents to predict future supply innovations in the economy: a higher degree of uncertainty implies
a more spread out probability density function of future supply shocks and, therefore, a larger
output volatility (Appendix IV). This section takes the case of real output uncertainty, but the
methodology could as well apply to monetary uncertainty (Bossone, 1997). By way of a simple
analysis of the model’s f.o.c.’s under the given assumptions, it is possible to assess the impact on
equilibrium allocation and prices of an increase in agents’ perceived uncertainty over future
output shocks in an economy with incomplete trust. With utility functions featuring the properties
described earlier and assuming that the agents anticipate real supply shocks to affect their
consumption, one has that
(8) t E[u’(Cτ )] − u’[t E (Cτ )] > 0
13

Proposition 5. An expected increase in output variability drives risk-averse agents to substitute
future with present consumption, and to shift their portfolio composition towards more liquid
assets enabling them to absorb negative consumption shocks with minimum suboptimal asset
sales. For equilibrium to be re-established in all markets, the price of current consumption and
the required (equilibrium) real rates on less liquid assets have to rise, while the required
(equilibrium) real rates on liquid assets have to adjust downward.
Basically, the way uncertainty works its effects through the economy in this model is via its
expected impact on the timing of asset trading and its related transaction costs. In times of higher
perceived uncertainty, agents expect discounts on less liquid assets to increase when they
suddenly want to make their portfolio more liquid.
In fact, there is no guarantee that the new equilibrium prices will be attained or sustainable,
if attained. Risk perceptions might be such as to lead the agents to deny their money to new
supply of less liquid liabilities at whatever price they are offered. Such a disequilibrium outcome
is consistent with financial market rationing phenomena typical of AI models of credit and capital
markets.
Finally, relations (11), (D1)-(D4) and the propositions above imply that:
Proposition 6. Improving the efficiency of the financial system helps the economy’s relative
prices better absorb exogenous shocks and
Proposition 7. Ceteris paribus, higher financial efficiency lowers the equilibrium relative price of
capital and the required premium on “catch-up” investment (see Proposition 4).
The last proposition suggests that, especially in the context of emerging economies, there is
a complementarity between financial and real sector development that can be exploited through
appropriate policies, a point which today is supported by considerable empirical evidence.19
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Part II. A policy view
II.1 Enhancing trust
Information limitation and asymmetries are a fact of life and agents may have strong
incentives to exploit them unethically. Also, information and its use are costly and not all agents
can afford them. Moreover, specialization of human activities is such that a world with symmetric
information is not attainable, nor would it be economically efficient (which is not to deny,
however, that at least in principle more information is always better than less). Indeed, a market
economy is essentially based on the need of each individual in society to rely on the specialized
knowledge of others as an efficient way to increase her welfare.
What matters, then, is that agents be able to rely on each other, and to have means to select
counterparties they can trust with using private information efficiently and fairly. As shown in
part I, the trust issue is crucial in financial transactions whereby anonymous agents trade
promises. Finance can thus be seen as the complex of institutions, instruments, norms and
infrastructure aimed to reduce transaction costs associated with trading promises between agents
who do not trust each other fully. In this respect, financial intermediaries act as bridges of trust
between agents, and their efficiency can be measured by their ability to reduce the costs involved
in bridging trust gaps (consistently with the definition of financial efficiency offered in part I).
The public sector can enhance trust in finance by improving the enforcement technologies
embodied in financial infrastructure (e.g., legal system, financial regulation, security in payment
and trading systems). But although strengthening enforcement technologies is necessary, it cannot
be the only solution since enforcing obligations becomes extremely costly if obligations run
contrary to private incentives. Fundamental improvements in financial efficiency can thus be
gained by eliciting good conduct as much as possible through market forces.20
II.2 Trust and reputational capital
As noted, in a world with incomplete trust, agents can earn positive quasi-rents by
specializing in financial intermediation between anonymous traders unwilling to trade promises
directly. In terms of part I model, intermediation by good bridges of trust reduces transaction
costs on the trading of promises and attracts demand for promises that would otherwise be
prohibitively expensive. In a competitive environment where agents seek to discriminate between
17

good and bad bridges of trust, it pays intermediaries to earn a good reputation and to signal such
reputation to the market. Where agents reward trust for honest and prudent behavior and punish
untrustworthy behavior, reputation links the intermediaries’ stream of future profits to their past
business conduct. Good intermediaries thus accumulate reputational capital which conveys to the
market the value of their commitment not to breach their (implicit or explicit) contracts with
clients and counterparties.21
Operationally, the reputational capital of a financial intermediary consists of a complex of
variables that signal the intermediary’s commitment and capacity to fulfil its obligations. The
most relevant variables are: the intermediary’s long-term mission, its market presence and past
performance, financial strength and profitability, organizational and governance structure,
capacity to manage financial and operational risks, track record of compliance with legal and
financial obligations, quality of service and advice delivered, quality of projects financed, quality
and ethics of management and personnel, and transparency of operations, resources invested to
stay in business. Other variables, external to the individual intermediaries, bear on their
reputational capital and include the quality of financial regulation and supervision and the
strength of law enforcement to which they are subject.
Klein and Leffler (1996) study the conditions under which the franchise of firms supplying
high quality of products exceeds their one-time wealth increase from distrustful behavior (i.e.,
selling to customers a quality less than contracted for). They show, inter alia, three important
results: First, they determine the price premium (above the competitive price) at which the value
of satisfied customers exceeds the return to the firm from cheating, thus motivating competitive
firms to honor high quality promises. This quality-assuring premium provides the supplying firm
with a perpetual stream of quasi-rents whose present value is greater than the profit from
cheating, that is: the net franchise value of the firm is positive, this being the value at loss if
misbehavior puts the intermediary out of business net of the one-time gain obtainable from
misbehaving.22
Second, firms accumulate non-salvageable (productive and/or nonproductive) capital assets
through which they signal to customers their commitment not to cheat. Such assets are part of the
firms’ reputational capital and represents the collateral that firms stand to lose if they supply less
than the anticipated quality (bank capital and reserves are an example). As the reputational capital
saves on the costs to evaluate trustworthiness, it gives the agents an incentive to pay a premium to
the firms for receiving the desired quality. In the case where the supplying firm is a financial
intermediary, such a premium reflects the value to the agents from minimizing the cost of trading
promises under incomplete trust:23 investors accept to pay a trust-assuring premium for being able
to entrust the intermediary agents with managing informational asymmetries vis-à-vis fund-
18

takers. 24 In fact, the intermediaries may extract extra-rents from fund-takers, too, in the form of
higher than competitive prices, as they enable them to economize on their reputational investment
in non-salvageable assets. This is consistent with the evidence reported by Rajan and Zingales
(1999) indicating that firms operating in more developed financial sectors undertake less fixed
capital formation.25
Third, as prices below the quality-assuring level decrease the demand for high quality
output, Klein and Leffler show that, under free market entry, firms compete on non-price terms
and seek to win customers by signaling a higher reputational capital. Under their model, it can be
shown that reputational capital accumulates (and new entries occur) within the industry to the
point where the business net franchise value is competed away.
Abstracting from ethical considerations (which, of course, influence the incentive structure
of individual intermediaries), what precedes suggests that investing in reputational capital is
meaningful only in repeated-game contexts with long business time horizons.26 Repeated and
extended dealings must take place over a period long enough to ensure that the net franchise
value is positive.27 Designing incentives to promote investment in reputational capital in the
financial sector requires to consider these features.
II.3 Incentives
The discussion in this section centers on incentives to induce trustworthy behavior in the
financial sector by soliciting agents’ self-interest in ways that generate self-sustaining
enforcement mechanisms of good behavior. Areas of public policy to strengthen financial
infrastructure are not dealt with here, although - as noted - their omission should not be
understood as neglecting their importance.
II.3.1 Investing in reputational capital: the role of regulation
Increasing the franchise of financial institutions is a necessary first step of financial sector
reform. Only a positive net franchise value from intermediation may attract investment in
reputational capital from financial institutions. Use of mild regulatory restraints on market
competition might increase the franchise value of domestic institutions, especially in least
developed countries and in those emerging from long periods of financial repression, or in deep
financial crisis and restructuring their financial sector (see Hellmann and Murdock 1995, and
Hellmann, Murdock and Stiglitz 1994, 1995, 1996).
In the banking sector, restraints such as (market-based) deposit rate ceilings and
restrictions on market entry may have large rent creation effects that would allow banks to raise
19

profits during the initial phase of reform. Hellmann et al., cit., show that the degree of restraints
necessary to produce significant rents is such that would not generate large financial market price
distortions. Also, to the extent that banks respond positively to restraints by investing rents in
reputational capital, price distortions would be partly eliminated by lower risk. In terms of Klein
and Leffler’s framework, restraints correspond to determining the trust-assuring price premium
exogenously while preventing free market entry. As a result, accumulation of reputational capital
is not induced by competition and must therefore be forced by regulation (see below).
Restraints should be accompanied, and eventually replaced, by restrictions on market
entry/exit based on reputational capital criteria. With exogenous reputational capital and
competitive or contestable markets, trust-assuring price premiums become endogenous and
restraints are no longer desirable. Reputational capital criteria could include minimum
requirements on financial capital, organizational, operational, and governance structures, risk-
management capacity, and conditions for fit and proper owners and managers. Licensing should
imply serious initial commitments from owners and managers wishing to enter the market,
showing their strong commitment to forsake one-time rent options from cheating or from
behaving imprudently. Issue of subordinated uninsured debt (see II.3.5) and publicly observable
discretionary guarantees (Boot et al., 1993) could also be required by regulation as signaling
devices for reputational capital and its dynamics.
Where restraints apply, reputational capital criteria could be used by regulators to make
markets contestable: licenses could be granted (transferred) to owners and managers on the basis
of their plans for a strong reputational capital. Transparent reputational capital criteria could be
used by regulators to decide on approval of changes in ownership and management resulting from
market takeovers, mergers, and reorganizations.
II.3.3 Investing in reputational capital: the role of economic capital
Economic capital is a core component of the reputational capital of financial
intermediaries. The capital adequacy ratios of the Basle Accord are a first essential step to induce
banks to accumulate capital vis-à-vis risks. As is well known, however, the static approach
embodied in the Accord may cause inefficiencies in resource allocation (Nickerson, 1995), and
result in either inadequate economic capital or undue costs on banks. Moreover, if capital ratios
are perceived exclusively as regulatory requirements, compliance with them could induce to self-
complacency from the intermediaries and to misleading signals to investors.
As the financial system develops in emerging economies, banks in these countries should
be advised to replace static capital ratios with more dynamic (and tailor-made) methods that
correlate financial capital more closely to risks. This would render the effects of risk
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